Tensors

Practical Leverage-Based Sampling for Low-Rank Tensor Decomposition

B. W. Larsen and T. G. Kolda, SIAM J. Matrix Analysis and Applications, 2022

Tensors Methods in Statistics

Had a great visit to University of Chicago, a highlight of which was getting a signed copy of the 2nd edition of Tensor Methods in Statistics.

Tensor Moments of Gaussian Mixture Models: Theory and Applications

J. M. Pereira, J. Kileel and T. G. Kolda, , 2022

Will the real Jennrich's Algorithm please stand up?

In many papers on tensor decomposition since 2014, the simultaneous diagonalization algorithm is incorrectly referenced as Jennrich’s algorithm. This method should not be attributed to Jennrich but instead cited as Leurgans, Ross, and Abel (1993).

Streaming Generalized Canonical Polyadic Tensor Decompositions

E. Phipps, N. Johnson and T. G. Kolda, , 2021

Stochastic Gradients for Large-Scale Tensor Decomposition

T. G. Kolda and D. Hong, SIAM Journal on Mathematics of Data Science, 2020

Faster Johnson-Lindenstrauss Transforms via Kronecker Products

R. Jin, T. G. Kolda and R. Ward, Information and Inference: A Journal of the IMA, 2020

Estimating Higher-Order Moments Using Symmetric Tensor Decomposition

S. Sherman and T. G. Kolda, SIAM Journal on Matrix Analysis and Applications, 2020

TuckerMPI: A Parallel C++/MPI Software Package for Large-scale Data Compression via the Tucker Tensor Decomposition

G. Ballard, A. Klinvex and T. G. Kolda, ACM Transactions on Mathematical Software, 2020

Generalized Canonical Polyadic Tensor Decomposition

D. Hong, T. G. Kolda and J. A. Duersch, SIAM Review, 2020